Intelligent LBE scene event setting system based on big data

Abstract

The invention relates to the technical field of big data processing, and particularly discloses an LBE scene event intelligent setting system based on big data. Comprising an amusement park area division module, an amusement park data acquisition module, an amusement park data preprocessing module, an amusement park data analysis module, a scene event intelligent analysis module, a data comprehensive analysis module, a scene event triggering module and a user interaction module. The amusement park data is collected, preprocessed and analyzed to obtain the tourist behavior influence index, the amusement facility operation state index and the amusement park environment quality index, then the amusement park scene event trigger index is obtained through analysis, and the amusement park events are intelligently set and managed by introducing the big data processing technology, so that the amusement park event management efficiency is improved. The attraction and competitiveness of the amusement park scene are improved, richer, more interesting and more personalized experience is provided for tourists, and the service quality of the amusement park and the satisfaction degree of the tourists are improved.

Description

Technical Field

[0001] This invention relates to the field of big data processing technology, and in particular to an LBE scene event intelligent setting system based on big data. Background Technology

[0002] In recent years, with the rapid development of technology and the continuous improvement of consumer experience demands, amusement parks, as an important form of Location-Based Entertainment (LBE), are facing unprecedented challenges and opportunities. Big data technology, as one of the core driving forces of the information age, is characterized by large data volume, diverse data types, high processing speed, and low value density. These characteristics enable big data technology to efficiently process and analyze massive amounts of data, extracting valuable information and providing strong support for decision-making and product optimization. In LBE scenarios, big data technology can capture real-time user location and behavior, the operational status of amusement facilities, and amusement park environmental data, providing a data foundation for the intelligent setting of scene events.

[0003] Traditional amusement park event design often relies on static planning and limited human intervention. This model is increasingly proving inadequate in the face of increasingly complex visitor behavior, equipment maintenance needs, and environmental changes. While amusement parks have gained deeper insights into visitor needs by collecting and analyzing large amounts of visitor behavior data, this approach is not as effective.

[0004] However, in practical use, it still has some shortcomings. For example, the data collected by traditional amusement park scene event settings remains at the basic level and cannot respond to the dynamic needs of tourists in real time to improve the overall experience. Most systems rely on preset rules for event settings, lacking intelligent analysis and dynamic adjustment capabilities. They cannot efficiently process massive amounts of data, resulting in slow system response, poor user experience, and an inability to adjust scene events in real time based on user behavior, leading to a monotonous user experience. In addition, traditional systems lack accurate analysis of tourists' real-time behavior, resulting in uneven resource allocation and limited tourist experience. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide an LBE scene event intelligent setting system based on big data to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an LBE scene event intelligent setting system based on big data, including an amusement park area division module, an amusement park data acquisition module, an amusement park data preprocessing module, an amusement park data analysis module, a scene event intelligent analysis module, a data comprehensive analysis module, a scene event triggering module, and a user interaction module.

[0007] Amusement Park Area Division Module: This module is used to identify large amusement parks as target areas, and divide them into monitoring sub-areas according to the geographical location of each amusement attraction, numbered sequentially as 1, 2, ..., i, ..., n;

[0008] The amusement park data acquisition module includes a visitor behavior information acquisition unit, an amusement facility information acquisition unit, and an amusement park environmental information acquisition unit. Each acquisition unit is used to collect comprehensive parameters of each monitoring sub-area and transmit the collected comprehensive parameters to the amusement park data preprocessing module.

[0009] Amusement Park Data Preprocessing Module: This module preprocesses the comprehensive parameters collected by the amusement park data acquisition module and transmits the preprocessed data to the amusement park data analysis module.

[0010] The amusement park data analysis module includes a visitor behavior analysis unit, an amusement facility analysis unit, and an amusement park environment analysis unit. It imports the data transmitted by the amusement park data preprocessing module into the corresponding mathematical model of the analysis unit, calculates the visitor behavior impact index, amusement facility operation status index, and amusement park environmental quality index for each monitoring sub-area, and transmits the calculation results to the scene event intelligent analysis module.

[0011] Scene event intelligent analysis module: It is used to compare the data transmitted by the amusement park data analysis module with the preset values ​​to determine whether there is an anomaly, and to activate the scene event intelligent setting system for the abnormal area;

[0012] Data Comprehensive Analysis Module: Based on the data obtained from the comparison and judgment by the Scene Event Intelligent Analysis Module, a mathematical model is established to calculate the scene event triggering index of the target area amusement park and transmit it to the scene event triggering module;

[0013] Scene event triggering module: It is used to compare and analyze the amusement park scene event triggering index calculated by the data comprehensive analysis module with the preset scene event triggering condition threshold, obtain the judgment result, and transmit the judgment result to the user interaction module;

[0014] User interaction module: Used to receive the results of activities and data comparison analysis in the amusement park scene, display them intuitively through the user interface, and provide play suggestions and safety warnings for managers and tourists.

[0015] Preferably, the visitor behavior information collection unit is used to collect visitor behavior parameters for each monitoring sub-area, including the number of visitors, queuing area area, queuing time, play time, number of tickets purchased, and total number of tickets purchased for each attraction; the amusement facility information collection unit is used to collect amusement facility operation parameters for each monitoring sub-area, including the actual output power of the motor, the input power of the motor, the no-load loss of the motor, the number of malfunctions, the malfunction time, the repair time, the operation time, the vibration frequency, the preset standard vibration frequency, the operating speed, and the preset safe operating speed; the amusement park environment information collection unit is used to collect amusement park environment parameters for each monitoring sub-area, including amusement park noise, the preset normal noise range, the amusement park ambient temperature, and the ambient temperature after an interval time t.

[0016] Preferably, the amusement park data preprocessing module preprocesses the collected data by using existing filtering techniques to perform noise reduction, outlier detection, and outlier processing, and also cleans and normalizes the collected data.

[0017] Preferably, the calculation model for the tourist behavior impact indicators of each monitoring sub-area is as follows:

[0018] Among them, TBII i Let Vc be the tourist behavior impact index for the i-th monitoring sub-region. i Let As represent the number of tourists in the i-th monitored sub-region. i Let Wt represent the queuing area of ​​the i-th monitoring sub-region. i Let Pt represent the queuing time for the i-th monitoring sub-region. i Let Ts represent the playtime in the i-th monitored sub-region. i Let Tst represent the number of tickets purchased in the i-th monitored sub-region. i This represents the total number of tickets purchased for the i-th monitoring sub-region.

[0019] Preferably, the calculation steps for the amusement facility operation status indicators of each monitoring sub-area are as follows:

[0020] Step S01: Calculate the energy efficiency of the amusement park's motors. The calculation model is as follows:

[0021] Among them, MEE i Let Op represent the motor energy efficiency of the amusement park rides in the i-th monitoring sub-region. i Let Ip represent the actual output power of the motors of the amusement rides in the i-th monitoring sub-region. iLet Nl represent the motor input power of the amusement ride in the i-th monitoring sub-region. i This represents the no-load loss of the motor of the amusement park ride in the i-th monitoring sub-region.

[0022] Step S02: Calculate the operational status indicators of the amusement facilities. The calculation model is as follows:

[0023] Among them, FOSI i Let MEE represent the operational status index of amusement facilities in the i-th monitoring sub-region. i Let Fc represent the motor energy efficiency of the amusement park rides in the i-th monitoring sub-region. i Let Ft represent the number of malfunctions of the amusement park rides in the i-th monitoring sub-region. i Let Rt represent the downtime of the amusement park ride in the i-th monitoring sub-region. i Ot represents the maintenance time of the amusement park rides in the i-th monitoring sub-region. i Vf represents the operating time of the amusement facilities in the i-th monitoring sub-region. i Let Vf0 represent the vibration frequency of the amusement ride in the i-th monitoring sub-region, and Ov represent the preset standard vibration frequency. i Ov0 represents the operating speed of the amusement facilities in the i-th monitoring sub-area, and Ov0 represents the preset safe operating speed.

[0024] Preferably, the calculation model for the amusement park environmental quality indicators of each monitoring sub-region is as follows:

[0025] Among them, AEQI i Let En be the environmental quality index of the amusement park in the i-th monitoring sub-region. i Let En0 represent the noise level in the i-th monitoring sub-region, and Tb represent the preset normal noise range. i1 Let Tb represent the ambient temperature of the i-th monitoring sub-region. i2 ΔT represents the ambient temperature after the interval t of the i-th monitoring sub-area, with the interval ranging from one hour to one and a half hours. ΔT represents the preset allowable temperature error value of the ambient temperature during the monitoring process.

[0026] Preferably, the specific method by which the scene event intelligent analysis module compares and judges the data transmitted by the amusement park data analysis module with preset values ​​is as follows:

[0027] Extract tourist behavior impact indicators (TBII) from each monitoring sub-region within the target area. i The formula for the fluctuation coefficient of tourist behavior influence in each monitored sub-region within the target area is obtained as follows: If the tourist behavior impact index TBII of the i-th monitoring sub-regioni If the visitor behavior event exceeds the preset threshold TBII0, it is determined that there is an anomaly in the visitor behavior event within the amusement park. The anomaly result is sent to the management personnel, and the amusement park scene event intelligent setting system is activated. Otherwise, it is determined that there is no anomaly in the visitor behavior event within the amusement park.

[0028] Extract the amusement facility operation status index (FOSI) of each monitoring sub-area within the target area. i The formula for the fluctuation coefficient of the amusement facility operation status change in each monitoring sub-area within the target area is obtained as follows: If the amusement facility operation status index FOSI of the i-th monitoring sub-region i If the value is less than the preset amusement facility operation status threshold FOSI0, it is determined that there is an abnormality in the operation status of the amusement facility in the amusement park. The abnormality result is sent to the management personnel and the amusement park scene event intelligent setting system is activated. Otherwise, it is determined that there is no abnormality in the operation status of the amusement facility in the amusement park.

[0029] Extract the amusement park environmental quality index (AEQI) for each monitoring sub-region within the target area. i The formula for the fluctuation coefficient of amusement park environmental quality change in each monitoring sub-region within the target area is obtained as follows: If the amusement park environmental quality index AEQI of the i-th monitoring sub-region i If the environmental quality is below the preset amusement park environmental quality threshold AEQI0, it is determined that there is an abnormality in the environmental quality of the amusement park. The abnormality result is sent to the management personnel, and the amusement park scene event intelligent setting system is activated. Otherwise, it is determined that there is no abnormality in the environmental quality of the amusement park.

[0030] Preferably, the calculation model for the event triggering index of the target area amusement park scene is as follows:

[0031] Wherein, CSAI represents the event trigger index for the amusement park scene in the target area, and TBII... i Let FOSI be the tourist behavior impact index for the i-th monitoring sub-region. i Let AEQI be the amusement facility operation status indicator for the i-th monitoring sub-region. i Let represent the environmental quality index of the amusement park in the i-th monitoring sub-region, and k1 represent a constant.

[0032] Preferably, the specific method for comparative analysis by the scene event triggering module is as follows:

[0033] The system obtains the amusement park scene event trigger index for the target area and compares it with the preset scene event trigger condition threshold. If the amusement park scene event trigger index is greater than the preset scene event trigger condition threshold, it indicates that the target area has met the scene event trigger condition. The system then activates the scene event intelligent setting system, issues an early warning signal, adjusts the scene events in the amusement park, and sends the abnormal area number to the relevant management personnel for review. Conversely, if the index is less than the preset threshold, it indicates that the target area has not met the scene event trigger condition.

[0034] The technical effects and advantages of this invention are as follows:

[0035] 1. With the help of big data analysis, the system can promptly detect potential safety hazards, such as equipment performance degradation and abnormal crowd gatherings, and respond quickly to prevent accidents and ensure the safety of tourists.

[0036] 2. This invention utilizes big data real-time analysis technology. The system can quickly respond to market changes, user feedback, and the needs of special periods such as holidays, automatically adjust or optimize scene event content, maintain the freshness and attractiveness of activities, extend the life cycle of LBE projects, and managers can easily grasp the real-time operating status of the amusement park through an intuitive data visualization interface, make scientific decisions quickly, and greatly improve the accuracy and efficiency of decision-making without relying on intuition or experience.

[0037] 3. This invention collects and preprocesses data from amusement parks to analyze indicators of visitor behavior impact, amusement facility operation status, and amusement park environmental quality. It then analyzes and obtains an amusement park scene event trigger index. By introducing big data processing technology, it enables intelligent setting and management of amusement park events, thereby enhancing the attractiveness and competitiveness of amusement park scenes, providing visitors with a richer, more interesting, and personalized experience, and improving the service quality and visitor satisfaction of amusement parks. Attached Figure Description

[0038] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0039] Figure 1 This is a schematic diagram of the overall system structure of the present invention.

[0040] Figure 2 This is a schematic diagram of the data acquisition unit structure of the amusement park data acquisition module of the present invention. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] Please see Figure 1-2 As shown, the present invention provides an LBE scene event intelligent setting system based on big data, including an amusement park area division module, an amusement park data acquisition module, an amusement park data preprocessing module, an amusement park data analysis module, a scene event intelligent analysis module, a data comprehensive analysis module, a scene event triggering module, and a user interaction module.

[0043] The output of the amusement park area division module is telecommunicationly connected to the input of the amusement park data acquisition module. The output of the amusement park data acquisition module is telecommunicationly connected to the input of the amusement park data preprocessing module. The output of the amusement park data preprocessing module is telecommunicationly connected to the input of the amusement park data analysis module. The output of the amusement park data analysis module is telecommunicationly connected to the input of the scene event intelligent analysis module. The output of the scene event intelligent analysis module is telecommunicationly connected to the input of the data comprehensive analysis module and the input of the user interaction module. The output of the data comprehensive analysis module is telecommunicationly connected to the input of the scene event triggering module and the input of the user interaction module. The output of the scene event triggering module is telecommunicationly connected to the input of the user interaction module.

[0044] The amusement park area division module is used to identify large amusement parks as target areas, divide them into monitoring sub-areas according to the geographical location of each amusement project, and number them sequentially as 1, 2, ..., i, ..., n;

[0045] The amusement park data acquisition module includes a visitor behavior information acquisition unit, an amusement facility information acquisition unit, and an amusement park environmental information acquisition unit. Each acquisition unit is used to collect comprehensive parameters of each monitoring sub-area and transmit the collected comprehensive parameters to the amusement park data preprocessing module.

[0046] In this embodiment, it should be specifically noted that the tourist behavior information collection unit is used to collect tourist behavior parameters for each monitoring sub-area, including the number of tourists, the area of ​​the queuing area, the queuing time, the playing time, the number of tickets purchased, and the total number of tickets purchased for the project.

[0047] The amusement facility information collection unit is used to collect the operating parameters of the amusement facilities in each monitoring sub-area, including the actual output power of the motor of the amusement facility, the input power of the motor of the amusement facility, the no-load loss of the motor of the amusement facility, the number of failures of the amusement facility, the failure time of the amusement facility, the maintenance time of the amusement facility, the running time of the amusement facility, the vibration frequency of the amusement facility, the preset standard vibration frequency, the running speed of the amusement facility, and the preset safe running speed.

[0048] The amusement park environmental information collection unit is used to collect amusement park environmental parameters for each monitoring sub-area, including amusement park noise, preset normal noise range, amusement park ambient temperature, and ambient temperature after an interval time t.

[0049] The amusement park data preprocessing module is used to preprocess the comprehensive parameters collected by the amusement park data acquisition module and transmit the preprocessed data to the amusement park data analysis module.

[0050] In this embodiment, it should be specifically explained that the amusement park data preprocessing module preprocesses the collected data by using existing filtering techniques to perform noise reduction, outlier detection, and outlier processing, and also cleans and normalizes the collected data.

[0051] The amusement park data analysis module includes a visitor behavior analysis unit, an amusement facility analysis unit, and an amusement park environment analysis unit. It imports the data transmitted by the amusement park data preprocessing module into the corresponding mathematical model of the analysis unit, calculates the visitor behavior impact index, amusement facility operation status index, and amusement park environmental quality index for each monitoring sub-area, and transmits the calculation results to the scene event intelligent analysis module.

[0052] In this embodiment, it should be specifically explained that the calculation model for the tourist behavior impact index of each monitoring sub-area is as follows:

[0053] Among them, TBII i Let Vc be the tourist behavior impact index for the i-th monitoring sub-region. i Let As represent the number of tourists in the i-th monitored sub-region. i Let Wt represent the queuing area of ​​the i-th monitoring sub-region. i Let Pt represent the queuing time for the i-th monitoring sub-region. i Let Ts represent the playtime in the i-th monitored sub-region. i Let Tst represent the number of tickets purchased in the i-th monitored sub-region. i This represents the total number of tickets purchased for the i-th monitoring sub-region.

[0054] In this embodiment, it is important to specifically explain that by analyzing tourist behavior parameters and measuring the ratio of tourist density per unit area, queuing time to play time, and the relationship between the number of tickets purchased and the number of tickets purchased for each attraction, the impact on amusement park scene events is analyzed based on a calculation model. Analyzing tourist density: If there are too many tourists and the queuing area is limited (i.e., the higher the density), the lower the tourist experience may be, and the greater the negative impact on the tourist experience. Analyzing the ratio of queuing time to play time: If the queuing time is too long relative to the play time (i.e., the larger the ratio), the lower the tourist satisfaction may be, and the greater the negative impact on the tourist experience. Analyzing the relationship between the number of tickets purchased and the number of tickets purchased for each attraction: If the total number of tickets sold in the amusement park is much greater than the number of tickets purchased for a specific attraction (i.e., the smaller the ratio), it may mean that some attractions are too crowded while others are relatively empty, indicating a more uneven distribution of resources, and a greater negative impact on the tourist experience.

[0055] In this embodiment, it should be specifically explained that the calculation steps for the amusement facility operation status indicators of each monitoring sub-area are as follows:

[0056] Step S01: Calculate the energy efficiency of the amusement park's motors. The calculation model is as follows:

[0057] Among them, MEE i Let Op represent the motor energy efficiency of the amusement park rides in the i-th monitoring sub-region. i Let Ip represent the actual output power of the motors of the amusement rides in the i-th monitoring sub-region. i Let Nl represent the motor input power of the amusement ride in the i-th monitoring sub-region. i This represents the no-load loss of the motor of the amusement park ride in the i-th monitoring sub-region.

[0058] Step S02: Calculate the operational status indicators of the amusement facilities. The calculation model is as follows:

[0059] Among them, FOSI i Let MEE represent the operational status index of amusement facilities in the i-th monitoring sub-region. i Let Fc represent the motor energy efficiency of the amusement park rides in the i-th monitoring sub-region. i Let Ft represent the number of malfunctions of the amusement park rides in the i-th monitoring sub-region. i Let Rt represent the downtime of the amusement park ride in the i-th monitoring sub-region. i Ot represents the maintenance time of the amusement park rides in the i-th monitoring sub-region. i Vf represents the operating time of the amusement facilities in the i-th monitoring sub-region. iLet Vf0 represent the vibration frequency of the amusement ride in the i-th monitoring sub-region, and Ov represent the preset standard vibration frequency. i Ov0 represents the operating speed of the amusement facilities in the i-th monitoring sub-area, and Ov0 represents the preset safe operating speed.

[0060] In this embodiment, it should be specifically explained that by analyzing the operating parameters of the amusement facility, including the actual output power of the motor, the input power of the motor, the no-load loss of the motor, the number of failures, the failure time, the maintenance time, the operating time, the vibration frequency, the preset standard vibration frequency, the operating speed, and the preset safe operating speed, an index is obtained to evaluate the performance of the amusement facility's operating status. This provides decision-making information for the intelligent setting system for amusement park scene events, ensuring the normal operation of the equipment and optimizing the user experience.

[0061] In this embodiment, it should be specifically explained that the calculation model for the amusement park environmental quality indicators of each monitoring sub-region is as follows:

[0062] Among them, AEQI i Let En be the environmental quality index of the amusement park in the i-th monitoring sub-region. i Let En0 represent the noise level in the i-th monitoring sub-region, and Tb represent the preset normal noise range. i1 Let Tb represent the ambient temperature of the i-th monitoring sub-region. i2 ΔT represents the ambient temperature after the interval t of the i-th monitoring sub-area, with the interval ranging from one hour to one and a half hours. ΔT represents the preset allowable temperature error value of the ambient temperature during the monitoring process.

[0063] In this embodiment, it should be specifically explained that by analyzing the temperature and noise levels of the environment, an index is obtained to evaluate the environmental quality of the amusement park, which can provide a good amusement environment for visitors and help improve their experience and satisfaction.

[0064] The scene event intelligent analysis module is used to compare the data transmitted by the amusement park data analysis module with preset values ​​to determine whether an anomaly has occurred, and to activate the scene event intelligent setting system for the abnormal area.

[0065] In this embodiment, it should be specifically explained that the scene event intelligent analysis module compares and judges the data transmitted by the amusement park data analysis module with the preset value in the following way:

[0066] Extract tourist behavior impact indicators (TBII) from each monitoring sub-region within the target area. iThe formula for the fluctuation coefficient of tourist behavior influence in each monitored sub-region within the target area is obtained as follows: If the tourist behavior impact index TBII of the i-th monitoring sub-region i If the visitor behavior event exceeds the preset threshold TBII0, it is determined that there is an anomaly in the visitor behavior event within the amusement park. The anomaly result is sent to the management personnel, and the amusement park scene event intelligent setting system is activated. Otherwise, it is determined that there is no anomaly in the visitor behavior event within the amusement park.

[0067] Extract the amusement facility operation status index (FOSI) of each monitoring sub-area within the target area. i The formula for the fluctuation coefficient of the amusement facility operation status change in each monitoring sub-area within the target area is obtained as follows: If the amusement facility operation status index FOSI of the i-th monitoring sub-region i If the value is less than the preset amusement facility operation status threshold FOSI0, it is determined that there is an abnormality in the operation status of the amusement facility in the amusement park. The abnormality result is sent to the management personnel and the amusement park scene event intelligent setting system is activated. Otherwise, it is determined that there is no abnormality in the operation status of the amusement facility in the amusement park.

[0068] Extract the amusement park environmental quality index (AEQI) for each monitoring sub-region within the target area. i The formula for the fluctuation coefficient of amusement park environmental quality change in each monitoring sub-region within the target area is obtained as follows: If the amusement park environmental quality index AEQI of the i-th monitoring sub-region i If the environmental quality is below the preset amusement park environmental quality threshold AEQI0, it is determined that there is an abnormality in the environmental quality of the amusement park. The abnormality result is sent to the management personnel, and the amusement park scene event intelligent setting system is activated. Otherwise, it is determined that there is no abnormality in the environmental quality of the amusement park.

[0069] In this embodiment, it should be specifically explained that the scene event intelligent analysis module activates the scene event intelligent setting system for abnormal areas in the following way:

[0070] By analyzing real-time pedestrian flow and visitor behavior data, the system intelligently schedules the opening hours of amusement rides, reducing visitor waiting times and improving the visitor experience. Through data analysis of amusement rides, the system intelligently sets the operational status of rides and links them with smart home appliances, lighting, and sound system control devices to reduce malfunctions and increase visitor satisfaction. By analyzing amusement park environmental data, the system intelligently sets voice broadcast times to push personalized information on scene events, special offers, and queue status for rides, improving visitor efficiency. Location tracking provides personalized guidance services and pushes information on nearby performances or activities.

[0071] The data comprehensive analysis module, based on the data obtained by the scene event intelligent analysis module through comparison and judgment, establishes a mathematical model to calculate the scene event triggering index of the target area amusement park and transmits it to the scene event triggering module.

[0072] In this embodiment, it should be specifically explained that the calculation model for the event triggering index of the target area amusement park scene is as follows:

[0073] Wherein, CSAI represents the event trigger index for the amusement park scene in the target area, and TBII... i Let FOSI be the tourist behavior impact index for the i-th monitoring sub-region. i Let AEQI be the amusement facility operation status indicator for the i-th monitoring sub-region. i Let represent the environmental quality index of the amusement park in the i-th monitoring sub-region, and k1 represent a constant.

[0074] The scene event triggering module is used to compare and analyze the amusement park scene event triggering index calculated by the data comprehensive analysis module with the preset scene event triggering condition threshold, obtain a judgment result, and transmit the judgment result to the user interaction module.

[0075] In this embodiment, it should be specifically explained that the specific method of comparison and analysis by the scene event triggering module is as follows:

[0076] The system obtains the amusement park scene event trigger index for the target area and compares it with the preset scene event trigger condition threshold. If the amusement park scene event trigger index is greater than the preset scene event trigger condition threshold, it indicates that the target area has met the scene event trigger condition. The system then activates the scene event intelligent setting system, issues an early warning signal, adjusts the scene events in the amusement park, and sends the abnormal area number to the relevant management personnel for review. Conversely, if the index is less than the preset threshold, it indicates that the target area has not met the scene event trigger condition.

[0077] The user interaction module is used to receive the results of activity and data comparison analysis in the amusement park scene, display them intuitively through the user interface, and provide play suggestions and safety warnings for managers and tourists.

[0078] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0079] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A LBE scene event intelligent setting system based on big data, characterized in that: include: Amusement park area division module: used to identify large amusement parks as target areas, divide them into monitoring sub-areas according to the geographical location of each amusement project, and number them 1, 2, ..., i, ..., n in sequence; Amusement park data collection module: including a visitor behavior information collection unit, an amusement facility information collection unit, and an amusement park environment information collection unit. Each collection unit is used to collect comprehensive parameters of each monitoring sub-area and transmit the collected comprehensive parameters to the amusement park data preprocessing module; Amusement park data preprocessing module: used to preprocess the comprehensive parameters collected by the amusement park data acquisition module, and transmit the preprocessed data to the amusement park data analysis module; The amusement park data analysis module includes a visitor behavior analysis unit, an amusement facility analysis unit, and an amusement park environment analysis unit. The module imports the data transmitted by the amusement park data preprocessing module into the corresponding analysis unit mathematical model, calculates the visitor behavior impact index, amusement facility operation status index, and amusement park environment quality index of each monitoring sub-area, and transmits the calculation results to the scene event intelligent analysis module; Scene event intelligent analysis module: used to compare the data transmitted by the amusement park data analysis module with the preset value to determine whether there is an abnormality, and activate the scene event intelligent setting system for the abnormal area; Data comprehensive analysis module: Based on the data obtained after comparison and judgment by the scene event intelligent analysis module, a mathematical model is established to calculate the scene event trigger index of the target area amusement park, and the index is transmitted to the scene event trigger module; Scene event trigger module: used to compare and analyze the amusement park scene event trigger index calculated by the data comprehensive analysis module with the preset scene event trigger condition threshold, obtain the judgment result, and transmit the judgment result to the user interaction module; User interaction module: used to receive the activities and data comparison and analysis results in the amusement park scene, intuitively display them through the user interface, and provide play suggestions and safety warnings to managers and tourists.

2. According to the big data-based LBE scene event intelligent setting system of claim 1, it is characterized by: The tourist behavior information collection unit is used to collect tourist behavior parameters of each monitoring sub-area, including the number of tourists, the area of ​​the queuing area, the queuing time, the playing time, the number of tickets purchased, and the total number of tickets purchased for each item; the amusement facility information collection unit is used to collect the amusement facility operation parameters of each monitoring sub-area, including the actual output power of the amusement facility motor, the input power of the amusement facility motor, the no-load loss of the amusement facility motor, the number of failures of the amusement facility, the failure time of the amusement facility, the maintenance time of the amusement facility, the operation time of the amusement facility, the vibration frequency of the amusement facility, the preset standard vibration frequency, the operation speed of the amusement facility, and the preset safe operation speed; the amusement park environment information collection unit is used to collect the amusement park environment parameters of each monitoring sub-area, including the amusement park noise, the preset normal noise range, the amusement park ambient temperature, and the ambient temperature after the interval time t.

3. The LBE scene event intelligent setting system based on big data according to claim 1 is characterized by: The amusement park data preprocessing module preprocesses the collected data by performing noise reduction, outlier detection, and outlier processing on the data through existing filtering technology, and cleans and normalizes the collected data.

4. The LBE scene event intelligent setting system based on big data according to claim 1 is characterized by: The calculation model of the tourist behavior impact index of each monitoring sub-area is as follows: Among them, TBII i Vc is the tourist behavior impact index of the ith monitoring sub-area, i As is the number of tourists in the ith monitoring sub-area, i It is expressed as the queuing area of ​​the ith monitoring sub-area, Wt i It is represented as the queuing time of the i-th monitoring sub-area, Pt i It is represented as the playing time of the ith monitoring sub-area, Ts i It is expressed as the number of tickets purchased in the ith monitoring sub-area, Tst i It is expressed as the total number of project tickets purchased in the i-th monitoring sub-area.

5. The LBE scene event intelligent setting system based on big data according to claim 1 is characterized by: The calculation steps of the amusement facility operation status index of each monitoring sub-area are as follows: Step S01: Calculate the motor energy consumption efficiency of the amusement facility. The calculation model is as follows: Among them, MEE i It is expressed as the motor energy consumption efficiency of the amusement facilities in the i-th monitoring sub-area, Op i It is represented by the actual output power of the motor of the amusement facility in the i-th monitoring sub-area, Ip i Nl is the motor input power of the amusement facilities in the i-th monitoring sub-area, i It is represented as the motor no-load loss of the amusement facilities in the i-th monitoring sub-area; Step S02: Calculate the operating status index of the amusement facility. The calculation model is as follows: Among them, FOSI i It is expressed as the operating status index of the amusement facilities in the i-th monitoring sub-area, MEE i Expressed as the motor energy consumption efficiency of the amusement facilities in the i-th monitoring sub-area, Fc i It is represented by the number of failures of the amusement facilities in the i-th monitoring sub-area, Ft i Rt is the failure time of the amusement facilities in the i-th monitoring sub-area. i It is represented as the maintenance time of the amusement facilities in the ith monitoring sub-area, Ot i Vf is the running time of the amusement facilities in the i-th monitoring sub-area. i is represented by the vibration frequency of the amusement facilities in the i-th monitoring sub-area, Vf0 is represented by the preset standard vibration frequency, Ov i It is represented as the running speed of the amusement facilities in the i-th monitoring sub-area, and Ov0 is represented as the preset safe running speed.

6. The LBE scene event intelligent setting system based on big data according to claim 1 is characterized by: The calculation model of the amusement park environmental quality index of each monitoring sub-area is as follows: Among them, AEQI i It is expressed as the amusement park environmental quality index of the i-th monitoring sub-area, En i is the noise of the ith monitoring sub-area, En0 is the preset normal noise range, Tb i1 It is represented as the ambient temperature of the ith monitoring sub-area, Tb i2 It represents the ambient temperature of the i-th monitoring sub-area after the interval time t, and the interval time ranges from one hour to one and a half hours. ΔT represents the allowable temperature error value of the preset ambient temperature during the monitoring process.

7. The LBE scene event intelligent setting system based on big data according to claim 1 is characterized by: The specific method in which the scene event intelligent analysis module compares and judges the data transmitted by the amusement park data analysis module with the preset value is: Extract the tourist behavior impact index TBII of each monitoring sub-area in the target area i , and the fluctuation coefficient formula of the tourist behavior impact change in each monitoring sub-area in the target area is obtained: If the tourist behavior impact index TBII of the i-th monitoring sub-area i If it is greater than the preset visitor behavior impact threshold TBII0, it is judged that there is an abnormality in the visitor behavior event in the amusement park, the abnormal result is sent to the management personnel, and the amusement park scene event intelligent setting system is started. Otherwise, it is judged that there is no abnormality in the visitor behavior event in the amusement park; Extract the FOSI of the amusement facility operation status index of each monitoring sub-area in the target area i , and obtain the fluctuation coefficient formula of the operation status of amusement facilities in each monitoring sub-area within the target area: If the amusement facility operation status index FOSI of the i-th monitoring sub-area i If the value is less than the preset threshold value FOSI0 for the operation status of the amusement facilities in the amusement park, it is judged that there is an abnormality in the operation status of the amusement facilities in the amusement park, the abnormal result is sent to the management personnel, and the amusement park scene event intelligent setting system is started; otherwise, it is judged that there is no abnormality in the operation status of the amusement facilities in the amusement park; Extract the amusement park environmental quality index AEQI of each monitoring sub-area in the target area i , and the formula for the fluctuation coefficient of the amusement park environmental quality change in each monitoring sub-area within the target area is obtained: If the amusement park environmental quality index AEQI of the i-th monitoring sub-area i If it is less than the preset amusement park environmental quality threshold AEQI0, it is judged that there is an abnormality in the environmental quality of the amusement park, the abnormal result is sent to the management personnel, and the amusement park scene event intelligent setting system is started. Otherwise, it is judged that there is no abnormality in the environmental quality of the amusement park.

8. The LBE scene event intelligent setting system based on big data according to claim 1 is characterized by: The target area amusement park scene event trigger index calculation model is as follows: Among them, CSAI is the trigger index of the amusement park scene event in the target area, TBII i It is expressed as the tourist behavior impact index of the i-th monitoring sub-area, FOSI i It is expressed as the operating status index of the amusement facilities in the i-th monitoring sub-area, AEQI i It is represented as the amusement park environmental quality index of the ith monitoring sub-area, and k1 is represented as a constant.

9. The LBE scene event intelligent setting system based on big data according to claim 1 is characterized by: The specific method of comparing and analyzing the scene event triggering module is as follows: Obtain the target area amusement park scene event trigger index and compare it with the preset scene event trigger condition threshold. If the amusement park scene event trigger index is greater than the preset scene event trigger condition threshold, it indicates that the target area has reached the scene event trigger condition. The scene event intelligent setting system is started, an early warning signal is issued, the scene events of the amusement park are regulated, and the abnormal area number is sent to the corresponding management personnel for review. Otherwise, it indicates that the target area has not reached the scene event trigger condition.

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